OpenRaiser/Intern-Atlas huggingface dataset:
Revisiting regular sequences in light of rational base numeration systems
Venue: arXiv cs.FL 2021
Year: 2021

Regular sequences generalize the extensively studied automatic sequences. Let $S$ be an abstract numeration system. When the numeration language $L$ is prefix-closed and regular, a sequence is said to be $S$-regular if the module generated by its $S$-kernel is finitely generated. In this paper, we give a new characterization of such sequences in terms of the underlying numeration tree $T(L)$ whose nodes are words of $L$. We may decorate these nodes by the sequence of interest following a breadth-first enumeration. For a prefix-closed regular language $L$, we prove that a sequence is $S$-regular if and only if the tree $T(L)$ decorated by the sequence is linear, i.e., the decoration of a node depends linearly on the decorations of a fixed number of ancestors. Next, we introduce and study regular sequences in a rational base numeration system, whose numeration language is known to be highly non-regular. We motivate and comment our definition that a sequence is $\frac{p}{q}$-regular if the underlying numeration tree decorated by the sequence is linear. We give the first few properties of such sequences, we provide a few examples of them, and we propose a method for guessing $\frac{p}{q}$-regularity. Then we discuss the relationship between $\frac{p}{q}$-automatic sequences and $\frac{p}{q}$-regular sequences. We finally present a graph directed linear representation of a $\frac{p}{q}$-regular sequence. Our study permits us to highlight the places where the regularity of the numeration language plays a predominant role.
Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention
Venue: arXiv cs.CV 2021
Year: 2021

Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of automatic trimap generation method of dilating foreground segmentation fluctuates with segmentation quality. Therefore, we argue that how to handle trade-off of additional information input is a major issue in automatic matting. This paper presents a semantic-guided automatic natural image matting pipeline with Trimap Generation Network and light-weight non-local attention, which does not need trimap and background as input. Specifically, guided by foreground segmentation, Trimap Generation Network estimates accurate trimap. Then, with estimated trimap as guidance, our light-weight Non-local Matting Network with Refinement produces final alpha matte, whose trimap-guided global aggregation attention block is equipped with stride downsampling convolution, reducing computation complexity and promoting performance. Experimental results show that our matting algorithm has competitive performance with state-of-the-art methods in both trimap-free and trimap-needed aspects.
Mining DEV for social and technical insights about software development
Venue: arXiv cs.SE 2021
Year: 2021

Software developers are social creatures: they communicate, collaborate, and promote their work in a variety of channels. Twitter, GitHub, Stack Overflow, and other platforms offer developers opportunities to network and exchange ideas. Researchers analyze content on these sites to learn about trends and topics in software engineering. However, insight mined from the text of Stack Overflow questions or GitHub issues is highly focused on detailed and technical aspects of software development. In this paper, we present a relatively new online community for software developers called DEV. On DEV users write long-form posts about their experiences, preferences, and working life in software, zooming out from specific issues and files to reflect on broader topics. About 50,000 users have posted over 140,000 articles related to software development. In this work, we describe the content of posts on DEV using a topic model, showing that developers discuss a rich variety and mixture of social and technical aspects of software development. We show that developers use DEV to promote themselves and their work: 83% link their profiles to their GitHub profiles and 56% to their Twitter profiles. 14% of users pin specific GitHub repos in their profiles. We argue that DEV is emerging as an important hub for software developers, and a valuable source of insight for researchers to complement data from platforms like GitHub and Stack Overflow.
Reliable Detection of Compressed and Encrypted Data
Venue: arXiv cs.CR 2021
Year: 2021

Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as entropy estimation, to identify encrypted fragments. However, modern content types use compression techniques which alter data distribution pushing it closer to the uniform distribution. The result is that current approaches exhibit unreliable encryption detection performance when compressed data appears in the dataset. Furthermore, proposed approaches are typically evaluated over few data types and fragment sizes, making it hard to assess their practical applicability. This paper compares existing statistical tests on a large, standardized dataset and shows that current approaches consistently fail to distinguish encrypted and compressed data on both small and large fragment sizes. We address these shortcomings and design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data. We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB. Our results highlight that EnCoD outperforms current approaches by a wide margin, with accuracy ranging from ~82 for 512B fragments up to ~92 for 8KB data fragments. Moreover, EnCoD can pinpoint the exact format of a given data fragment, rather than performing only binary classification like previous approaches.
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.
What Do We See: An Investigation Into the Representation of Disability in Video Games
Venue: arXiv cs.HC 2021
Year: 2021

There has been a large body of research focused on the representation of gender in video games. Disproportionately, there has been very little research in respect to the representation of disability. This research was aimed at examining the representation of disabled characters through a method of content analysis of trailers combined with a survey of video gamers. The overall results showed that disabled characters were under-represented in videogames trailers, and respondents to the survey viewed disabled characters as the least represented group. Both methods of research concluded that the representation of disabled characters was low. Additionally, the characters represented were predominantly secondary, non-playable characters not primary. However, the research found that the defined character type was a mixture of protagonists and antagonists, bucking the standard view of disabled characters in video games.
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this paper, we propose a new benchmark dataset, named \textit{Topo-boundary}, for offline topological road-boundary detection. The dataset contains 25,295 $1000\times1000$-sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks. We also design a new entropy-based metric for connectivity evaluation, which could better handle noises or outliers. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset. We also propose a new imitation-learning-based baseline which is enhanced from our previous work. The superiority of our enhancement is demonstrated from the comparison. The dataset and our-implemented code for the baselines are available at \texttt{\url{https://tonyxuqaq.github.io/Topo-boundary/}}.
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems
Venue: arXiv cs.LG 2021
Year: 2021

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.
On Deeply Critical Oriented Cliques
Venue: arXiv cs.DM 2021
Year: 2021

In this work we consider arc criticality in colourings of oriented graphs. We study deeply critical oriented graphs, those graphs for which the removal of any arc results in a decrease of the oriented chromatic number by $2$. We prove the existence of deeply critical oriented cliques of every odd order $n\geq 9$, closing an open question posed by Borodin et al. (Journal of Combinatorial Theory, Series B, 81(1):150-155, 2001). Additionally, we prove the non-existence of deeply critical oriented cliques among the family of circulant oriented cliques of even order.
Static and Dynamic Failure Localization through Progressive Network Tomography
Venue: arXiv cs.NI 2021
Year: 2021

We aim at assessing the states of the nodes in a network by means of end-to-end monitoring paths. The contribution of this paper is twofold. First, we consider a static failure scenario. In this context, we aim at minimizing the number of probes to obtain failure identification. To face this problem, we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures, given the observation, whereas the other approximates these values by means of a novel failure centrality metric. Secondly, we adapt these two strategies to a dynamic failure scenario where nodes states can change throughout a monitoring period. By means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. Our performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.
Digital Twin Based Disaster Management System Proposal: DT-DMS
Venue: arXiv cs.AI 2021
Year: 2021

The damage and the impact of natural disasters are becoming more destructive with the increase of urbanization. Today's metropolitan cities are not sufficiently prepared for the pre and post-disaster situations. Digital Twin technology can provide a solution. A virtual copy of the physical city could be created by collecting data from sensors of the Internet of Things (IoT) devices and stored on the cloud infrastructure. This virtual copy is kept current and up to date with the continuous flow of the data coming from the sensors. We propose a disaster management system utilizing machine learning called DT-DMS is used to support decision-making mechanisms. This study aims to show how to educate and prepare emergency center staff by simulating potential disaster situations on the virtual copy. The event of a disaster will be simulated allowing emergency center staff to make decisions and depicting the potential outcomes of these decisions. A rescue operation after an earthquake is simulated. Test results are promising and the simulation scope is planned to be extended.
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
Venue: arXiv cs.CV 2021
Year: 2021

Tremendous progress in deep generative models has led to photorealistic image synthesis. While achieving compelling results, most approaches operate in the two-dimensional image domain, ignoring the three-dimensional nature of our world. Several recent works therefore propose generative models which are 3D-aware, i.e., scenes are modeled in 3D and then rendered differentiably to the image plane. This leads to impressive 3D consistency, but incorporating such a bias comes at a price: the camera needs to be modeled as well. Current approaches assume fixed intrinsics and a predefined prior over camera pose ranges. As a result, parameter tuning is typically required for real-world data, and results degrade if the data distribution is not matched. Our key hypothesis is that learning a camera generator jointly with the image generator leads to a more principled approach to 3D-aware image synthesis. Further, we propose to decompose the scene into a background and foreground model, leading to more efficient and disentangled scene representations. While training from raw, unposed image collections, we learn a 3D- and camera-aware generative model which faithfully recovers not only the image but also the camera data distribution. At test time, our model generates images with explicit control over the camera as well as the shape and appearance of the scene.
Models and numbers: Representing the world or imposing order?
Venue: arXiv cs.CY 2021
Year: 2021

We argue for a foundational epistemic claim and a hypothesis about the production and uses of mathematical epidemiological models, exploring the consequences for our political and socio-economic lives. First, in order to make the best use of scientific models, we need to understand why models are not truly representational of our world, but are already pitched towards various uses. Second, we need to understand the implicit power relations in numbers and models in public policy, and, thus, the implications for good governance if numbers and models are used as the exclusive drivers of decision making.
Approximation Schemes for Multiperiod Binary Knapsack Problems
Venue: arXiv cs.DS 2021
Year: 2021

An instance of the multiperiod binary knapsack problem (MPBKP) is given by a horizon length $T$, a non-decreasing vector of knapsack sizes $(c_1, \ldots, c_T)$ where $c_t$ denotes the cumulative size for periods $1,\ldots,t$, and a list of $n$ items. Each item is a triple $(r, q, d)$ where $r$ denotes the reward of the item, $q$ its size, and $d$ its time index (or, deadline). The goal is to choose, for each deadline $t$, which items to include to maximize the total reward, subject to the constraints that for all $t=1,\ldots,T$, the total size of selected items with deadlines at most $t$ does not exceed the cumulative capacity of the knapsack up to time $t$. We also consider the multiperiod binary knapsack problem with soft capacity constraints (MPBKP-S) where the capacity constraints are allowed to be violated by paying a penalty that is linear in the violation. The goal is to maximize the total profit, i.e., the total reward of selected items less the total penalty. Finally, we consider the multiperiod binary knapsack problem with soft stochastic capacity constraints (MPBKP-SS), where the non-decreasing vector of knapsack sizes $(c_1, \ldots, c_T)$ follow some arbitrary joint distribution but we are given access to the profit as an oracle, and we choose a subset of items to maximize the total expected profit, i.e., the total reward less the total expected penalty. For MPBKP, we exhibit a fully polynomial-time approximation scheme with runtime $\tilde{\mathcal{O}}\left(\min\left\{n+\frac{T^{3.25}}{ε^{2.25}},n+\frac{T^{2}}{ε^{3}},\frac{nT}{ε^2},\frac{n^2}ε\right\}\right)$ that achieves $(1+ε)$ approximation; for MPBKP-S, the $(1+ε)$ approximation can be achieved in $\mathcal{O}\left(\frac{n\log n}ε\cdot\min\left\{\frac{T}ε,n\right\}\right)$; for MPBKP-SS, a greedy algorithm is a 2-approximation when items have the same size.
Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix
Venue: arXiv cs.LG 2021
Year: 2021

DNN structures are continuously developing and achieving high performances in classification problems. Also, it is observed that success rates obtained with DNNs are higher than those obtained with traditional neural networks. In addition, one of the advantages of DNNs is that there is no need to spend an extra effort to determine the features; the CNN automatically extracts the features from the dataset during the training. Besides their benefits, the DNNs have the following three major drawbacks among the others: (i) Researchers have struggled with over-fitting and under-fitting issues in the training of DNNs, (ii) determination of even a coarse structure for the DNN may take days, and (iii) most of the time, the proposed network structure is too large to be too bulky to be used in real time applications. We have modified the training and structure of DNN to increase the classification performance, to decrease the number of nodes in the structure, and to be used with less number of hyper parameters. A minimum distance network (MDN) following the last layer of the convolutional neural network (CNN) is used as the classifier instead of a fully connected neural network (FCNN). In order to strengthen the training of the CNN, we suggest employing Walsh function. We tested the performances of the proposed DNN (named as DivFE) on the classification of ECG, EEG, heart sound, detection pneumonia in X-ray chest images, detection of BGA solder defects, and patterns of benchmark datasets (MNIST, IRIS, CIFAR10 and CIFAR20). In different areas, it has been observed that a higher classification performance was obtained by using the DivFE with less number of nodes.
Force-and-moment-based Model Predictive Control for Achieving Highly Dynamic Locomotion on Bipedal Robots
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we propose a novel framework on force-and-moment-based Model Predictive Control (MPC) for dynamic legged robots. Specifically, we present a formulation of MPC designed for 10 degree-of-freedom (DoF) bipedal robots using simplified rigid body dynamics with input forces and moments. This MPC controller will calculate the optimal inputs applied to the robot, including 3-D forces and 2-D moments at each foot. These desired inputs will then be generated by mapping these forces and moments to motor torques of 5 actuators on each leg. We evaluate our proposed control design on physical simulation of a 10 degree-of-freedom (DoF) bipedal robot. The robot can achieve fast walking speed up to 1.6 m/s on rough terrain, with accurate velocity tracking. With the same control framework, our proposed approach can achieve a wide range of dynamic motions including walking, hopping, and running using the same set of control parameters.
An Online Survey on the Perception of Mediated Social Touch Interaction and Device Design
Venue: arXiv cs.HC 2021
Year: 2021

Social touch is essential for our social interactions, communication, and well-being. It has been shown to reduce anxiety and loneliness; and is a key channel to transmit emotions for which words are not sufficient, such as love, sympathy, reassurance, etc. However, direct physical contact is not always possible due to being remotely located, interacting in a virtual environment, or as a result of a health issue. Mediated social touch enables physical interactions, despite the distance, by transmitting the haptic cues that constitute social touch through devices. As this technology is fairly new, the users' needs and their expectations on a device design and its features are unclear, as well as who would use this technology, and in which conditions. To better understand these aspects of the mediated interaction, we conducted an online survey on 258 respondents located in the USA. Results give insights on the type of interactions and device features that the US population would like to use.
Real-time Data Infrastructure at Uber
Venue: arXiv cs.DC 2021
Year: 2021

Uber's business is highly real-time in nature. PBs of data is continuously being collected from the end users such as Uber drivers, riders, restaurants, eaters and so on everyday. There is a lot of valuable information to be processed and many decisions must be made in seconds for a variety of use cases such as customer incentives, fraud detection, machine learning model prediction. In addition, there is an increasing need to expose this ability to different user categories, including engineers, data scientists, executives and operations personnel which adds to the complexity. In this paper, we present the overall architecture of the real-time data infrastructure and identify three scaling challenges that we need to continuously address for each component in the architecture. At Uber, we heavily rely on open source technologies for the key areas of the infrastructure. On top of those open-source software, we add significant improvements and customizations to make the open-source solutions fit in Uber's environment and bridge the gaps to meet Uber's unique scale and requirements. We then highlight several important use cases and show their real-time solutions and tradeoffs. Finally, we reflect on the lessons we learned as we built, operated and scaled these systems.